{"files"=>["https://ndownloader.figshare.com/files/2089854"], "description"=>"<p>Each plot compares, for each decoder, an estimate of the user’s learned encoder parameters against the optimal encoder parameters for a given subject, aggregated across signal cases. The optimal parameters for the two decoders are unrelated. A reference line is depicted on each plot and correlation coefficients are provided for each decoder (All correlations are significantly different from zero, <i>p</i> < < .01). Each point corresponds to a comparison between how much weight the user gave each channel vs how much weight the channel would have been given if optimally learned. Points near zero correspond to channels not heavily used. While perfect match would not be expected, the relatively strong correlations indicate that both decoder classes are learned reasonably well by all subjects.</p>", "links"=>[], "tags"=>["BCI", "decoding algorithm", "performance advantages", "end effector", "prosthesis simulator", "decoding system", "computer screen", "approach yields", "user", "experiments support", "control signals", "optimized decoder"], "article_id"=>1432052, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Josh Merel", "Donald M. Pianto", "John P. Cunningham", "Liam Paninski"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004288.g007", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_All_subjects_learned_the_coding_schemes_/1432052", "title"=>"All subjects learned the coding schemes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-01 02:50:05"}

{"files"=>["https://ndownloader.figshare.com/files/2089860", "https://ndownloader.figshare.com/files/2089861", "https://ndownloader.figshare.com/files/2089862"], "description"=>"<div><p>Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme (\"encoding model\") and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages.</p></div>", "links"=>[], "tags"=>["BCI", "decoding algorithm", "performance advantages", "end effector", "prosthesis simulator", "decoding system", "computer screen", "approach yields", "user", "experiments support", "control signals", "optimized decoder"], "article_id"=>1432056, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Josh Merel", "Donald M. Pianto", "John P. Cunningham", "Liam Paninski"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1004288.s001", "https://dx.doi.org/10.1371/journal.pcbi.1004288.s002", "https://dx.doi.org/10.1371/journal.pcbi.1004288.s003"], "stats"=>{"downloads"=>2, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Encoder_Decoder_Optimization_for_Brain_Computer_Interfaces_/1432056", "title"=>"Encoder-Decoder Optimization for Brain-Computer Interfaces", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-06-01 02:50:05"}

{"files"=>["https://ndownloader.figshare.com/files/2089858"], "description"=>"<p>The cartoon on the left depicts the OPS: (a) Actual overt movements of the user are detected. (b) Synthetic neural activity is generated in some fashion derived from the overt movements. (c) The decoder sees only the simulated activity and provides a decoded estimate of the cursor position. (d) The cursor is placed on the screen along with the target permitting visual feedback. The flow diagrams on the right compare how data is generated in a real BCI versus the OPS and the relationship between the variables. While there is no direct link relating intended kinematics and overt movement (or volition), these variables are conditionally related. If the user desires to produce a certain decoded kinematics and doing so requires some modulations of the neural activity which are controlled by the overt movement, then the user should modulate overt movement in a way that reflects their intended kinematics.</p>", "links"=>[], "tags"=>["BCI", "decoding algorithm", "performance advantages", "end effector", "prosthesis simulator", "decoding system", "computer screen", "approach yields", "user", "experiments support", "control signals", "optimized decoder"], "article_id"=>1432054, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Josh Merel", "Donald M. Pianto", "John P. Cunningham", "Liam Paninski"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004288.g008", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_OPS_setup_is_depicted_and_related_to_real_BCI_/1432054", "title"=>"OPS setup is depicted and related to real BCI.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-01 02:50:05"}